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异质分布下的多维潜在分组结构AI 解读

This paper aims to identify the multi-dimensional latent grouped heterogeneity of distributional effects. We consider a panel quantile regression model with additive cross-section and time fixed effects. The cross-section effects and quantile slope coefficients are both characterized by grouped patterns of heterogeneity, but each unit can belong to different groups for cross-section effects and slopes. We propose a composite-quantile approach to jointly estimate…

回归折点设计中的分位数处理效应AI 解读

The literature on regression kink designs develops identification results for average effects of continuous treatments (Nielsen et al., 2010, American Economic Journal: Economic Policy 2, 185–215; Card et al., 2015, Econometrica 83, 2453–2483), average effects of binary treatments (Dong, 2018, Jump or Kink? Identifying Education Effects by Regression Discontinuity Design without the Discontinuity), and quantile-wise effects of continuous treatments (Chiang and Sasaki, 2019,…

不连续与折点激励分配机制下加权平均处理效应的识别及小波估计AI 解读

This paper studies identification, estimation, and inference of a weighted average treatment effect (W-ATE) parameter in a class of switching regime models, where the agent’s selection of treatment is affected by either a discontinuous or kink incentive assignment mechanism and some unobservable characteristic. For each assignment mechanism, we (i) establish identification and propose a local wavelet estimator of the W-ATE; (ii) establish asymptotic properties of the local…

高斯转换机制模型中潜在结果相关系数的推断AI 解读

We propose estimators of sharp bounds on the correlation coefficient between potential outcomes in the Gaussian switching regime model and develop an asymptotically uniformly valid and non-conservative confidence set for the true correlation coefficient. A boundary-interior-category selection procedure is proposed to deal with discontinuity of the pointwise asymptotic distribution of estimators of the sharp bounds. Our confidence set is easy to implement: it takes the form…

估计转换机制模型与处理效应参数的灵活参数方法AI 解读

In this paper, we propose a flexible, parametric class of switching regime models allowing for both skewed and fat-tailed outcome and selection errors. Specifically, we model the joint distribution of each outcome error and the selection error via a newly constructed class of multivariate distributions which we call generalized normal mean–variance mixture distributions. We extend Heckman’s two-step estimation procedure for the Gaussian switching regime model to the new…